With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA‐small molecule interactions has become an indispensable tool for RNA‐targeted drug discovery. The current models for RNA–ligand binding have mainly focused on the docking‐and‐scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein–ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics‐based and knowledge‐based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep‐learning approaches has led to new tools for predicting RNA‐small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA–ligand docking and their advantages and disadvantages. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics RNA‐targeted drug discovery requires the synergy of enhanced sampling and accurate scoring with fast computational speed. The distinct aspects of RNA–ligand docking compared to protein–ligand docking pose unique challenges, which demand a new generation of molecular docking models. This review presents an overview of recently developed RNA–ligand molecular docking methods for RNA‐targeted drug discovery.